Setting the Standard for Medical AI Quality
Our quality team, consisting of healthcare professionals and data scientists, goes beyond static medical exam benchmarks to continuously evaluate and improve the real-world performance of MedAsk. We design and open-source custom benchmarks that more accurately reflect the use cases our customers face in practice.
These benchmarks assess multiple dimensions of medical quality, such as diagnostic accuracy, triage accuracy, and medical coding precision. To enable objective comparisons with other solutions, we incorporate hundreds of clinical cases sourced from peer-reviewed literature.
Transparency is a core principle in everything we do. That’s why all of MedAsk’s benchmark results are publicly available on our GitHub.
Diagnostic Accuracy
On SymptomCheck Bench, our open-source benchmark for evaluating LLM-powered symptom checkers using a dataset of 400 peer-reviewed clinical vignettes, MedAsk achieves a diagnostic accuracy of 90.6%—the highest performance among all systems evaluated on this dataset.
Triage Accuracy
On the gold-standard Semigran dataset, MedAsk outperformes both leading LLMs like OpenAI’s o1 and top symptom checkers with:
- 82.7% overall triage accuracy
- 96% emergency case accuracy
- 94.2% safety of advice—the highest of any system tested
Unlike LLMs that default to “better safe than sorry,” MedAsk strikes the right balance between safety and precision, making it a strong fit for real-world triage at scale.
Triage Accuracy
On the gold-standard Semigran dataset, MedAsk outperformes both leading LLMs like OpenAI’s o1 and top symptom checkers with:
- 82.7% overall triage accuracy
- 96% emergency case accuracy
- 94.2% safety of advice—the highest of any system tested
Unlike LLMs that default to “better safe than sorry,” MedAsk strikes the right balance between safety and precision, making it a strong fit for real-world triage at scale.
Medical Coding Accuracy
MedAsk outperforms GPT-4o in mapping diseases to the correct ICD-10 codes, with a 16% higher accuracy rate on 948 real-world cases. Beyond improved precision, it also reduces serious errors—cutting complete mismatches by 4x. Unlike GPT-4o, MedAsk doesn’t hallucinate codes when uncertain. Instead, it returns null values, ensuring greater reliability and safety in clinical workflows.
